



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def A    (							__UpperCamelCase       )    ->   Any:
			A__	  =     SwinConfig(image_size=192       )

			if "base" in model_name:
						A__	  =     6
						A__	  =     128
						A__	  =     (2, 2, 18, 2)
						A__	  =     (4, 8, 16, 32)
			elif "large" in model_name:
						A__	  =     12
						A__	  =     192
						A__	  =     (2, 2, 18, 2)
						A__	  =     (6, 12, 24, 48)
			else:
						raise ValueError('Model not supported, only supports base and large variants'       )

			A__	  =     window_size
			A__	  =     embed_dim
			A__	  =     depths
			A__	  =     num_heads

			return config


def A    (							__UpperCamelCase       )    ->   List[str]:
			if "encoder.mask_token" in name:
						A__	  =     name.replace('encoder.mask_token'			,						'embeddings.mask_token'       )
			if "encoder.patch_embed.proj" in name:
						A__	  =     name.replace('encoder.patch_embed.proj'			,						'embeddings.patch_embeddings.projection'       )
			if "encoder.patch_embed.norm" in name:
						A__	  =     name.replace('encoder.patch_embed.norm'			,						'embeddings.norm'       )
			if "attn.proj" in name:
						A__	  =     name.replace('attn.proj'			,						'attention.output.dense'       )
			if "attn" in name:
						A__	  =     name.replace('attn'			,						'attention.self'       )
			if "norm1" in name:
						A__	  =     name.replace('norm1'			,						'layernorm_before'       )
			if "norm2" in name:
						A__	  =     name.replace('norm2'			,						'layernorm_after'       )
			if "mlp.fc1" in name:
						A__	  =     name.replace('mlp.fc1'			,						'intermediate.dense'       )
			if "mlp.fc2" in name:
						A__	  =     name.replace('mlp.fc2'			,						'output.dense'       )

			if name == "encoder.norm.weight":
						A__	  =     'layernorm.weight'
			if name == "encoder.norm.bias":
						A__	  =     'layernorm.bias'

			if "decoder" in name:
						pass
			else:
						A__	  =     'swin.' + name

			return name


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Dict:
			for key in orig_state_dict.copy().keys():
						A__	  =     orig_state_dict.pop(UpperCAmelCase_       )

						if "attn_mask" in key:
									pass
						elif "qkv" in key:
									A__	  =     key.split('.'       )
									A__	  =     int(key_split[2]       )
									A__	  =     int(key_split[4]       )
									A__	  =     model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

									if "weight" in key:
												A__	  =     val[:dim, :]
												A__	  =     val[
												    dim : dim * 2, :
												]
												A__	  =     val[-dim:, :]
									else:
												A__	  =     val[
												    :dim
												]
												A__	  =     val[
												    dim : dim * 2
												]
												A__	  =     val[
												    -dim:
												]
						else:
									A__	  =     val

			return orig_state_dict


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   List[str]:
			A__	  =     torch.load(UpperCAmelCase_			,						map_location='cpu'       )['model']

			A__	  =     get_swin_config(UpperCAmelCase_       )
			A__	  =     SwinForMaskedImageModeling(UpperCAmelCase_       )
			model.eval()

			A__	  =     convert_state_dict(UpperCAmelCase_			,						UpperCAmelCase_       )
			model.load_state_dict(UpperCAmelCase_       )

			A__	  =     'http://images.cocodataset.org/val2017/000000039769.jpg'

			A__	  =     ViTImageProcessor(size={'height': 192, 'width': 192}       )
			A__	  =     Image.open(requests.get(UpperCAmelCase_			,						stream=UpperCAmelCase_       ).raw       )
			A__	  =     image_processor(images=UpperCAmelCase_			,						return_tensors='pt'       )

			with torch.no_grad():
						A__	  =     model(**UpperCAmelCase_       ).logits

			print(outputs.keys()       )
			print('Looks ok!'       )

			if pytorch_dump_folder_path is not None:
						print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''       )
						model.save_pretrained(UpperCAmelCase_       )

						print(f'''Saving image processor to {pytorch_dump_folder_path}'''       )
						image_processor.save_pretrained(UpperCAmelCase_       )

			if push_to_hub:
						print(f'''Pushing model and image processor for {model_name} to hub'''       )
						model.push_to_hub(f'''microsoft/{model_name}'''       )
						image_processor.push_to_hub(f'''microsoft/{model_name}'''       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--model_name''',
							    default='''swin-base-simmim-window6-192''',
							    type=str,
							    choices=['''swin-base-simmim-window6-192''', '''swin-large-simmim-window12-192'''],
							    help='''Name of the Swin SimMIM model you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--checkpoint_path''',
							    default='''/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth''',
							    type=str,
							    help='''Path to the original PyTorch checkpoint (.pth file).''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
							)
							parser.add_argument(
							    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
							)

							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()
							convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""


import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=7					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		None
       if token is not None:
              UpperCamelCase       			=		{"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

       # The id of a workflow (not of a workflow run)
       UpperCamelCase       			=		"636036"

       UpperCamelCase       			=		F"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
       # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
       url += F"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"

       UpperCamelCase       			=		requests.get(UpperCAmelCase_		,   headers=UpperCAmelCase_					).json()

       return result["workflow_runs"]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Any:







       """simple docstring"""
       UpperCamelCase       			=		get_daily_ci_runs(UpperCAmelCase_					)
       UpperCamelCase       			=		None
       for workflow_run in workflow_runs:
              if workflow_run["status"] == "completed":
                     UpperCamelCase       			=		workflow_run["id"]
                     break

       return workflow_run_id



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				int:







       """simple docstring"""
       UpperCamelCase       			=		get_last_daily_ci_runs(UpperCAmelCase_					)
       if workflow_run_id is not None:
              UpperCamelCase       			=		get_artifacts_links(worflow_run_id=UpperCAmelCase_		,   token=UpperCAmelCase_					)
              for artifact_name in artifact_names:
                     if artifact_name in artifacts_links:
                            UpperCamelCase       			=		artifacts_links[artifact_name]
                            download_artifact(
                                artifact_name=UpperCAmelCase_		,   artifact_url=UpperCAmelCase_		,   output_dir=UpperCAmelCase_		,   token=UpperCAmelCase_					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       get_last_daily_ci_artifacts(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)

       UpperCamelCase       			=		{}
       for artifact_name in artifact_names:
              UpperCamelCase       			=		os.path.join(UpperCAmelCase_		,   F"{artifact_name}.zip"					)
              if os.path.isfile(UpperCAmelCase_					):
                     UpperCamelCase       			=		{}
                     with zipfile.ZipFile(UpperCAmelCase_					) as z:
                            for filename in z.namelist():
                                   if not os.path.isdir(UpperCAmelCase_					):
                                          # read the file
                                          with z.open(UpperCAmelCase_					) as f:
                                                 UpperCamelCase       			=		f.read().decode("UTF-8"					)

       return results





